We examine a gap between availability and accessibility as it relates to the adoption of AI-Mediated Communication (AI-MC) tools - including voice-assisted communication, language correction, predictive text suggestion, transcription, translation, and personalized language learning tools - by conducting an online survey. [(Open Access Link - available until 10/01/21)](https://authors.elsevier.com/a/1dZSh2f%7EUWF2hd)
We provide a review of five methodological developments poised to provide increased understanding in the domain of social media and well-being: (1) the use of longitudinal and experimental designs; (2) the adoption of behavioural (rather than self-report) measures of SMU; (3) a shift away from aggregate use; (4) the emergence of an idiographic media effects paradigm; and (5) the use of formal modelling and machine learning. [(PsyArXiv Link)](https://psyarxiv.com/exhru/)
Through a meta-analysis, we investigate the relationship between self-reported and device-logged media use. [(Open Access Link)](https://rdcu.be/ckK81)
Two randomized experiments (n = 1036) provide evidence that a commercially deployed AI changes how people interact with and perceive one another in pro-social and anti-social ways. We find that even though AI can increase communication efficiency and improve interpersonal perceptions, it risks changing users’ language production and continues to be viewed negatively. [(PDF - arXiv)](https://arxiv.org/pdf/2102.05756.pdf)
We examine the possibility that different associations between social media and depression may be caused by the survey design itself, not by underlying differences in depression. [(Open Access Link)](https://journals.sagepub.com/doi/full/10.1177/2056305120961784)
To better understand the impact of AI in employment-related contexts, we conducted two experiments (the first pre-registered) investigating how the use of AI by applicants influences their job opportunities.